# HTML Tables to Markdown (GFM) for RAG & LLMs (`awesome_highboy/tableforge`) Actor
Extract every HTML table from any URL into clean, deterministic GitHub-Flavored Markdown (GFM). Auto-detects headers (or synthesizes col1..N), escapes pipes, collapses whitespace, and stamps each table with an sha256 hash for dedup & idempotency. RAG / embeddings / LLM ready. Same HTML, same output.
- **URL**: https://apify.com/awesome\_highboy/tableforge.md
- **Developed by:** [Adam](https://apify.com/awesome_highboy) (community)
- **Categories:** Developer tools, AI, Automation
- **Stats:** 0 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $1.00 / 1,000 table extracteds
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## TableForge: Docs -> Queryable GFM Tables
Turn the HTML tables buried in your pages into clean, deterministic, RAG-ready GitHub-Flavored Markdown.
### What it does
TableForge fetches each URL you provide, parses the returned HTML with a real DOM (jsdom), and extracts **every** `` on the page. Each table is converted into a clean GitHub-Flavored Markdown (GFM) table:
- Cell text is whitespace-collapsed and trimmed, and `|` characters are escaped so the Markdown stays valid.
- If the table has a header row (`` or a leading `| ` row), those headers are used; otherwise synthetic headers (`col1`, `col2`, ...) are generated so every table is well-formed.
- A standard GFM header separator (`| --- | --- |`) is emitted, making the output ready to drop into Markdown, paste into an LLM prompt, or feed an embeddings/RAG pipeline.
- Every table gets a deterministic `content_hash` (`sha256:` + 64 hex) computed over its GFM text, so identical tables always produce identical hashes for dedup and idempotency.
The conversion is fully deterministic: the same HTML in always yields the same Markdown and the same hash out. Nothing is summarized, rewritten, or hallucinated; missing cells are emitted as empty, never invented.
### Input
| Field | Type | Required | Description |
| --- | --- | --- | --- |
| `urls` | array of strings | yes | Page URLs whose tables you are authorized to extract (your own, authorized, or public pages). |
| `ownership_attestation` | boolean | yes | Must be `true` to confirm you own or are authorized to extract from these pages. If `false`/omitted, the run is rejected before any work and bills `$0`. |
### Output
Records are pushed to the dataset. There are two `record_type` values:
**`table`** — one record per extracted table:
| Field | Type | Description |
| --- | --- | --- |
| `record_type` | string | `"table"` |
| `source_url` | string | The page the table came from. |
| `table_index` | integer | Zero-based index of the table within the page. |
| `gfm_table` | string | The full GitHub-Flavored Markdown table. |
| `column_headers` | string[] | The header cells (real or synthesized `col1..colN`). |
| `row_count` | integer | Number of body rows (excluding the header). |
| `content_hash` | string | Deterministic `sha256:<64 hex>` over the GFM text. |
**`run_summary`** — one record per run:
| Field | Type | Description |
| --- | --- | --- |
| `record_type` | string | `"run_summary"` |
| `pages_processed` | integer | Pages successfully fetched and scanned. |
| `tables_extracted` | integer | Total tables converted across all pages. |
A page that fails to fetch is skipped entirely (it contributes no records and is not counted). A page that fetches successfully but contains no tables is counted in `pages_processed` but adds no table records. Either way, pages with zero tables are **never** billed.
### Pricing
TableForge uses Apify Pay-Per-Event. You are billed only for:
| Event | Price (USD) | When it fires |
| --- | --- | --- |
| `actor_run_start` | $0.005 | Once per run, after the ownership and paid-plan gates pass. |
| `table_extracted` | $0.001 | Once per table successfully converted to GFM (the billed unit). |
Pages are scanned but **not** billed (no double-charging), and failed or empty pages cost $0.
**Example run:** scan 10 pages that together contain 80 tables -> `$0.005` (run start) + `80 x $0.001` = **$0.085 total**.
### Why this Actor
- **Deterministic + idempotent.** Output is a pure function of the input HTML. Every table carries an `sha256:` content hash over its Markdown, so you can dedup, cache, and detect changes reliably across runs.
- **No hallucination.** Tables are parsed structurally from the DOM and reproduced faithfully. Missing cells are emitted empty, never fabricated; nothing is paraphrased or summarized.
- **Ownership attestation gate.** A run cannot proceed unless you attest you are authorized to extract from the pages; without it, the run is rejected before any work with zero billing.
- **Embeddings/RAG-ready by design.** Clean GFM with preserved (or synthesized) headers, escaped pipes, and per-table hashes drops straight into LLM prompts, vector stores, and Markdown docs.
---
*This Actor is AI-authored and operated under the publisher's LLC. It uses Apify's `Actor.charge()` solely to **bill the customer** for the events above; the Actor contains no payout or money-out capability of any kind.*
# Actor input Schema
## `urls` (type: `array`):
URLs whose tables you are authorized to extract.
## `ownership_attestation` (type: `boolean`):
I own/am authorized to extract from these pages (REQUIRED)
## Actor input object example
```json
{
"urls": [
"https://www.w3.org/TR/html401/struct/tables.html"
],
"ownership_attestation": true
}
````
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"urls": [
"https://www.w3.org/TR/html401/struct/tables.html"
],
"ownership_attestation": true
};
// Run the Actor and wait for it to finish
const run = await client.actor("awesome_highboy/tableforge").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"urls": ["https://www.w3.org/TR/html401/struct/tables.html"],
"ownership_attestation": True,
}
# Run the Actor and wait for it to finish
run = client.actor("awesome_highboy/tableforge").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"urls": [
"https://www.w3.org/TR/html401/struct/tables.html"
],
"ownership_attestation": true
}' |
apify call awesome_highboy/tableforge --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=awesome_highboy/tableforge",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "HTML Tables to Markdown (GFM) for RAG & LLMs",
"description": "Extract every HTML table from any URL into clean, deterministic GitHub-Flavored Markdown (GFM). Auto-detects headers (or synthesizes col1..N), escapes pipes, collapses whitespace, and stamps each table with an sha256 hash for dedup & idempotency. RAG / embeddings / LLM ready. Same HTML, same output.",
"version": "0.1",
"x-build-id": "TW1VOY6eSVdpWoISl"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/awesome_highboy~tableforge/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-awesome_highboy-tableforge",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/awesome_highboy~tableforge/runs": {
"post": {
"operationId": "runs-sync-awesome_highboy-tableforge",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/awesome_highboy~tableforge/run-sync": {
"post": {
"operationId": "run-sync-awesome_highboy-tableforge",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"urls",
"ownership_attestation"
],
"properties": {
"urls": {
"title": "Page URLs (your own / authorized / public)",
"type": "array",
"description": "URLs whose tables you are authorized to extract.",
"items": {
"type": "string"
}
},
"ownership_attestation": {
"title": "I own/am authorized to extract from these pages (REQUIRED)",
"type": "boolean",
"description": "I own/am authorized to extract from these pages (REQUIRED)",
"default": false
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```
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